Public Lab Research note

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Testing the Mobile DustDuino

by Willie |

Why a mobile dust sensor?

Clara Rondonuwu, the project manager of the Indonesian GeoJournalism site, recently made some modifications to the DustDuino prototype to equip the device for mobile use.

Clara’s concept for the mobile DustDuino was inspired by the work presented by researcher Josh Apte at a sensor journalism training carried out by the Earth Journalism Network in May 2014. Most traditional evaluations of air quality use a few high-quality sensors to model exposure over large areas. While these methods accurately measure the overall air quality of a city, or ambient air, Apte felt that these measures didn’t give the full story – missing out on what types and levels of pollutants people actually breathe as they go about their daily activities in various parts of the city.

This UC Berkeley Lab post describes how Apte took to the streets of New Delhi to study the spatial patterns of air quality throughout the city and identify areas that are disproportionately affected by air pollutants. After attaching a video camera and a GPS to an auto-rickshaw, along with sensors that measured the levels of fine particulate matter (PM2.5), black carbon and ultrafine particles—three pollutants that are among the most dangerous to human health—Apte drove the vehicle through the streets and highways of Delhi for four consecutive months during the morning and evening rush hours.

Apte’s results highlighted an issue that most commuters had a feeling was true. The concentrations of all three pollutants were significantly higher on the roads than in the ambient air, indicating that air quality is disproportionately poor in these areas. Apte summarized his research by stating that “one’s exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high income countries.”

Mobile DustDuino monitors—when combined with other tools like video and GPS—may be used to identify micro-environments in which pollutant levels are disproportionately high. While one sensor alone doesn’t necessarily create a large enough dataset to give actionable results, once potential hotspots are identified, a greater number of static sensors can be used to monitor the air quality in the targeted area.

The attempt and results: Office Desktop to Car Dashboard

Clara’s conversion of the low-cost DustDuino from a static to a mobile monitor required three steps and allows DustDuino to broadcast data wherever a cellular mobile network is available. The first step was to create a mobile hotspot, by using a smart phone or tablet which has this capability or purchasing mobile WiFi. Second, the WiFi module within DustDuino must be configured to connect to this mobile wireless network. Finally, a portable power source must be found for the sensor. That's all it takes to get this dust sensor on the road.

PM_spikes_clara.jpg Readings from the mobile DustDuino streamed to Xivley

DustDuino designer Matt Schroyer has suggestions for mobile power sources as well as a more detailed explanation of each step. The mobile sensor's utility and potential applications are greatly increased without having to worry about staying within range of a WiFi connection and power source.

Questions and next steps

Clara experimented with mobile data collection while driving around in a car through Jakarta’s notoriously bad traffic but these modifications also make it easier to use DustDuino places away from a power outlet like parks, the countryside, or near industrial operations -- anywhere with a 3G cellular network and a potential air quality issue. If you plan on experimenting in a similar way, maps of 3G coverage can be seen found on the MobileWorldLive website.

particulate-sensing air-quality dustduino dust particulates indonesia particulate traffic jakarta



That's pretty neat. How airflow will be controlled, as I would expect a moving vehicle to increase particulate measurements as a matter of the airflow change.

I think collecting data on travel speed to control for any effects is important. In Apte's research he used impellers to control airflow. I know @nshapiro has some low-cost impeller suggestions.

Thanks Matt. This is definitely at its onset but I wanted to share the Apte inspiration. He did a number of things to ensure that the quality of the information was scientifically valid. The impeller suggestion is really sound. As a journalist I also like the use of video and GPS to corroborate the data stream.

Hi willie! Its a great project! I haven't read the Apte documentation, but mobile sensing does create some difficulties in terms of maintaining the accuracy of your data. if the vehicle speed is what forces the air through the sensor then you may get high readings at higher rates of speed that are not necessarily comparable. If you used a vacuum pump to move air through the device you will get the same flow rate no matter the speed and you can then get more accurate and comparable data.

I have a very overdue research note to write about the air pumps that we are using for a formaldehyde monitoring project, basically you can take a tetra whisper 100 aquarium pump and open it up with a Philips head screw driver and reverse the diaphragm (super simple and I can explain that later). That turns the pump into a vacuum that has a pretty consistent 300 ml/minute pull on it when attached to the formaldehyde tubes we are using. but regardless hooking up a low cost vacuum (like the hacked pump i just described) could help with data consistency! Happy to explain more, as I will post the full research note in a couple weeks.

thanks for sharing, Nick! I didn't want to give away your secret. We're looking at several situations (like particles, H2S) where controlled airflow may be important. I'm excited to try this out, and am interested in what @ewilder and @sophie think too.

Having an inexpensive mobile monitor would be wonderful. In fact, having an inexpensive stationary monitor would be wonderful. But one needs credible evidence that they are doing what is expected. Accuracy, precision, and calibration must be attended to in comparison to a reference monitor. The original Dust Duino description questions the ability to monitor at time intervals less than an hour. That would make mobile monitoring problematic. Has any work been done to characterize these instruments?

Hi Willie Is the actual sensor used in the video a dustduino? I was surprised by the high sampling frequency of a low cost DIY device...

The influence of air velocity with readings is a fascinating topic. Mobile air pollution monitors implemented on trucks currently used in some cities obviously must have some sophisticated ways to take care of this. Anyone know how they do it?

There are apparently some misconceptions here. As I read it, the monitor used by Apte was a commercial DustTrak not a dustduino and his research had nothing to do with a dustduino. He's doing graduate and decently funded research. And the monitor was placed within his vehicle which makes sense because he was concerned with the exposure of someone in a vehicle using the roadways. This was not a test of a mobile dustduino.

Jeff is correct. The video shows Dr Apte's set up for his work in Delhi. I included it as a kind of inspiration for the start of our work and something to aspire to. We still have a lot of work to do to create a solid reference for calibrating the mobile data collected. Apte's auto-rickshaw kit contained thousands of dollars worth of equipment and he did sustained mobile monitoring for 4 months to reach his conclusions. I think his research demonstrates some best practice for us to follow for mobile sensing.

Another well financed monitoring report that includes mobile monitoring is by David Snyder from UW Stevens Point: LADCO Midwest Wood Smoke Study: Among other things this report demonstrates the informative value of short time interval monitoring data in contrast to hourly or 24 hour averages.

woah, nice idea with the aquarium pump, looking forward to it.


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